Opening Remarks  & Introduction

The Introduction to the problem of Medical Insurance Fraud

• Medical Insurance Fraud and its implications for society and economies.
• Major Types of Fraud, waste, and abuse in healthcare insurance industry

The Introduction to Data Analytics / Data Science
The Evidence of Healthcare Fraud and How to Collect it / Discuss what data to analyze
What is Data Analytics?

• Descriptive, Diagnostic, Predictive and Prescriptive
• Importance of Descriptive & Diagnostic Analytics
• When we need Predictive & Prescriptive Analytics

Fundamentals of Descriptive Analytics – Exploratory Data Analysis (EDA) / BI Reports / Visualization Dashboards

• How to do efficient EDA?
• How to develop effective and outcome driven dashboards?
• What KPIs to report?

AI / Machine Learning 101 and Its Applications In Healthcare
Fundamentals of Predictive & Prescriptive Analytics – Machine Learning

• Major types of Machine Learning (e.g., classification, regression, association rules, clustering, text analytics, anomaly / outlier detection, etc) and relevant examples
• Common use cases where Machine Learning is used but you may not know it
• Common health care use cases where data analytics is used today

Data Analytics Applications in Health care Fraud Waste and Abuse – EDA, Reporting & Dashboards
Review of previous day learning sessions and data analytics discussed

• What kind of data is available for the data analytics
• What kind of EDA is useful in health care fraud?
• How to convert your FWA expertise into business rules that could be run automatically every day / hour / week? – Case studies and Roundtable discussion
• Examples of visualization dashboards / tools to help fraud investigation
• Possible demo of some of these visualization tools / dashboards

Predictive / Prescriptive Analytics in Action to combat Health Care Fraud
How advanced data analytics could be used to fight health care FWA?

• Business rules – quick review from yesterday
• Classification models – detecting variations of know health care fraud
• Anomaly / Outlier Detection – detecting novel types of health care fraud

Doing your homework – assets, marital and financial status, bankruptcies/divorces/ substance abuse data

Predictive / Prescriptive Analytics in Health Care Fraud Detection - Case Studies and Round Table Discussions

• Data Analytics for Post-pay vs. Pre-Pay
• Reviewing actual use cases of successful investigations from advanced data analytics
• Round table discussion on possible fraud use cases and how data analytics could help
• Round table discussion and review of the course

Quiz and discussion of responses